Computing devices output recommendations to expose items that are likely of interest to a user, even if the user is unaware of the items' existence. For instance, a video streaming service outputs movie or television show recommendations, an online store outputs product recommendations, and so forth. Many services have replaced conventional search engines with automatic recommendations as a primary means for discovering content items. Accordingly, systems strive to tailor recommendations to individual end users, such that recommended items are actually of interest.
Conventional recommendation systems leverage historical data describing explicit interactions between user profiles and items. Explicit interactions include item view-view relationships, which describe items that were viewed together in a single browsing session. Other explicit interactions include view-bought relationships, which describe at least one item that was purchased after being viewed. Alternatively or additionally, explicit interactions include item bought-bought relationships, which describe items that were purchased together in a single browsing session. Conventional approaches build models from this explicit interaction data and use these models to determine a recommendation for a user profile.
Conventional recommendation systems, however, are unable to generate recommendations for user profiles that do not have historic explicit interaction data. Similarly, conventional recommendation systems are unable to account for implicit interactions between user profiles and items. Implicit interactions refer to interactions that do not explicitly indicate affinity for certain items. For instance, implicit interactions may include a number of times an item was viewed, an amount of time spent reading a news article, a percentage of a video watched, and so forth. As a result, conventional approaches for digital recommendations fail to account for significant interaction data that would otherwise influence a recommendation decision. Accordingly, conventional approaches for automatic item recommendations are limited to considering only certain types of interaction data, which often results in outputting irrelevant recommendations.